Automatic semantic modeling of indoor scenes from low-quality RGB-D data using contextual information

ACM Trans. Graph., 2014.

Cited by: 88|Bibtex|Views121|DOI:https://doi.org/10.1145/2661229.2661239
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Other Links: dl.acm.org|dblp.uni-trier.de

Abstract:

We present a novel solution to automatic semantic modeling of indoor scenes from a sparse set of low-quality RGB-D images. Such data presents challenges due to noise, low resolution, occlusion and missing depth information. We exploit the knowledge in a scene database containing 100s of indoor scenes with over 10,000 manually segmented an...More

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